[JESUSROYETH]

--model.capture_segment_heads true replays the segmentation head's existing prediction calls and its first-order backward instead of re-running them. The native arithmetic and the unused-parameter gradient boundaries stay exactly as they are. It's off by default. Coverage is narrow too: one CUDA device, BF16 mixed precision, torch 2.9+, automatic optimisation, no gradient accumulation.

I ran five alternating native/replay pairs on an isolated g2-standard-8 NVIDIA L4, against main e4db46d:

Full run	Median (range), s
Native	202.794 (201.536–204.709)
Replay	169.130 (168.167–169.786)

So the full run takes 16.60% less time on COCO val2017 (384 training / 32 disjoint validation images, 80 classes, nano, batch 4, 640 px, 10 epochs). That number covers the whole run: construction, caching, recording, training, EMA, validation and checkpoints. Both arms already had prepared caching, device HSV, epoch prefetch and orthogonalisation/update replay turned on, so the gain sits on top of those. Torch 2.9.1+cu129, driver 580.178.04, Lightning 2.6.6. Whole-process wall time falls 16.16%, and peak reserved CUDA memory goes up a bit, 6084→6474 MiB.

Model/optimiser/scheduler/EMA/loss byte hashes and the validation metrics match across the five pairs, and a separate COCO prediction check matches raw bytes too. Full pre-commit passes, so does the offline make test: 4343 tests, 96.43% coverage against the unchanged 96% floor. The focused recorder suite on the L4 reports 134 passed; on torch 2.4 it reports 126 passed and 8 skipped. A real Trainer regression checks that graph launches stay correct after EMA validation. What I didn't run this round: the full make gate, the accelerator goldens, and a long-run accuracy check. Those stay unverified.

That gain is limited to this batch-4 workload, though. An earlier single batch-16 screen only gave 2.01%, and I haven't re-checked larger-batch performance against current source yet.

Clean-room acknowledged: native expressions, R43 and our measurements; no method arithmetic or frozen value changes.

WP: 193e
Provenance: R43; native prediction calls and measured CUDA profiles
Assumptions: none
Gate: tests/ptl/test_segment_head_capture.py

[kilo-code-bot]

Code Review Summary

Status: No Issues Found | Recommendation: Merge

Files Reviewed (13 files)

• CHANGELOG.md
• docs/ROADMAP.md
• docs/TRAINING.md
• docs/engineering/phase-17-pose-objective-parity.md
• docs/work_packages/wp-193c.md
• docs/work_packages/wp-193d.md
• docs/work_packages/wp-193e.md
• docs/work_packages/wp-194.md
• src/lucid_yolo/models/heads/detect.py
• src/lucid_yolo/ptl/module.py
• src/lucid_yolo/ptl/segment_head_capture.py
• tests/ptl/test_segment_head_capture.py
• tests/ptl/test_segment_head_capture_guards.py

Previous Review Summary

Current summary above is authoritative. Previous snapshots are kept for context only.

Previous review

This review did not finish. The model reached its output limit before it
could write the review — a reasoning model can spend the whole budget thinking.
Re-run the review, or lower the model's thinking effort, and it should get
further. Any inline comments below are from an earlier review.

---

_{Reviewed by free · Input: 57.5K · Output: 2.4K · Cached: 385.3K}

[Copilot]

🔵 Needs a closer look

CUDA forward/backward replay warrants human review, with the full gate, accelerator goldens and long-run accuracy checks still outstanding.

No open issues 🎉

What changed in this PR

Adds opt-in CUDA replay for segmentation predictions and their first-order backward work, preserving native arithmetic and unused-mask gradient behavior.

Changes:

• Introduces guarded BF16 prediction replay with fit-scoped cleanup and native fallback.
• Adds regression tests for exact outputs, gradients, compatibility and lifecycle behavior.
• Documents configuration, measured performance, memory costs and validation limits.

File	Description
tests/​ptl/​test_segment_head_capture.py	Tests replay correctness, guards and cleanup.
src/​lucid_yolo/​ptl/​segment_head_capture.py	Implements prediction recording and replay.
src/​lucid_yolo/​ptl/​module.py	Integrates the option and lifecycle callbacks.
src/​lucid_yolo/​models/​heads/​detect.py	Adds prediction scheduling without changing arithmetic.
docs/​work_packages/​wp-194.md	Updates work-package navigation.
docs/​work_packages/​wp-193e.md	Records implementation, measurements and validation gaps.
docs/​work_packages/​wp-193c.md	Links the new dependent work package.
docs/​TRAINING.md	Documents usage, requirements and limitations.
docs/​ROADMAP.md	Tracks WP-193e and outstanding validation.
docs/​engineering/​phase-17-pose-objective-parity.md	Includes the new engineering record.
CHANGELOG.md	Announces the opt-in feature.

_{Review effort: Balanced}